import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


A						=			datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
				import pyspark





@dataclass
class     lowerCAmelCase__     (				datasets.BuilderConfig			):


		'''simple docstring'''






		lowerCAmelCase_     =   None



def 	lowerCamelCase					(	UpperCamelCase     :				"pyspark.sql.DataFrame"			,       UpperCamelCase     :				List[int]			,       )   ->    List[str]:
		import pyspark

		def generate_fn():
				_lowerCamelCase				   =  df.select('*'			,       pyspark.sql.functions.spark_partition_id().alias('part_id'							)							)
				for partition_id in partition_order:
						_lowerCamelCase				   =  df_with_partition_id.select('*'							).where(F"""part_id = {partition_id}"""							).drop('part_id'							)
						_lowerCamelCase				   =  partition_df.collect()
						_lowerCamelCase				   =  0
						for row in rows:
								yield F"""{partition_id}_{row_id}""", row.asDict()
								row_id += 1

		return generate_fn





class     lowerCAmelCase__     (				_BaseExamplesIterable			):


		'''simple docstring'''






		def __init__(       self     :     List[str]     ,  snake_case__     :     "pyspark.sql.DataFrame"     ,  snake_case__     :     Dict=None     ,  )      ->	Any:
				_lowerCamelCase				   =  df
				_lowerCamelCase				   =  partition_order or range(self.df.rdd.getNumPartitions()		)
				_lowerCamelCase				   =  _generate_iterable_examples(self.df     ,  self.partition_order		)
		def __iter__(       self     :     Tuple		)      ->	str:
				yield from self.generate_examples_fn()
		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     np.random.Generator		)      ->	"SparkExamplesIterable":
				_lowerCamelCase				   =  list(range(self.df.rdd.getNumPartitions()		)		)
				generator.shuffle(snake_case__		)
				return SparkExamplesIterable(self.df     ,  partition_order=snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	"SparkExamplesIterable":
				_lowerCamelCase				   =  self.split_shard_indices_by_worker(snake_case__     ,  snake_case__		)
				return SparkExamplesIterable(self.df     ,  partition_order=snake_case__		)



		@property
		def   _snake_case       (       self     :     str		)      ->	int:
				return len(self.partition_order		)





class     lowerCAmelCase__     (				datasets.DatasetBuilder			):


		'''simple docstring'''






		lowerCAmelCase_     =   SparkConfig
		def __init__(       self     :     int     ,  snake_case__     :     "pyspark.sql.DataFrame"     ,  snake_case__     :     str = None     ,  snake_case__     :     str = None     ,  **snake_case__     :     Optional[Any]     ,  )      ->	str:
				import pyspark

				_lowerCamelCase				   =  pyspark.sql.SparkSession.builder.getOrCreate()
				_lowerCamelCase				   =  df
				_lowerCamelCase				   =  working_dir

				super().__init__(
				    cache_dir=snake_case__     ,  config_name=str(self.df.semanticHash()		)     ,  **snake_case__     ,  )
		def   _snake_case       (       self     :     Dict		)      ->	Dict:
				# Returns the path of the created file.
				def create_cache_and_write_probe(snake_case__     :     Optional[int]		):
						# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
						# already exist.
						os.makedirs(self._cache_dir     ,  exist_ok=snake_case__		)
						_lowerCamelCase				   =  os.path.join(self._cache_dir     ,  'fs_test' + uuid.uuida().hex		)
						# Opening the file in append mode will create a new file unless it already exists, in which case it will not
						# change the file contents.
						open(snake_case__     ,  'a'		)
						return [probe_file]

				if self._spark.conf.get('spark.master'     ,  ''		).startswith('local'		):
						return

				# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
				# accessible to the driver.
				# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
				if self._cache_dir:
						_lowerCamelCase				   =  (
						    self._spark.sparkContext.parallelize(range(1		)     ,  1		).mapPartitions(snake_case__		).collect()
						)
						if os.path.isfile(probe[0]		):
								return

				raise ValueError(
				    'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir'		)
		def   _snake_case       (       self     :     str		)      ->	Optional[Any]:
				return datasets.DatasetInfo(features=self.config.features		)
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     datasets.download.download_manager.DownloadManager		)      ->	str:
				return [datasets.SplitGenerator(name=datasets.Split.TRAIN		)]
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]		)      ->	List[str]:
				import pyspark

				def get_arrow_batch_size(snake_case__     :     Any		):
						for batch in it:
								yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]}		)

				_lowerCamelCase				   =  self.df.count()
				_lowerCamelCase				   =  df_num_rows if df_num_rows <= 1_0_0 else 1_0_0
				# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
				_lowerCamelCase				   =  (
				    self.df.limit(snake_case__		)
				    .repartition(1		)
				    .mapInArrow(snake_case__     ,  'batch_bytes: long'		)
				    .agg(pyspark.sql.functions.sum('batch_bytes'		).alias('sample_bytes'		)		)
				    .collect()[0]
				    .sample_bytes
				    / sample_num_rows
				)
				_lowerCamelCase				   =  approx_bytes_per_row * df_num_rows
				if approx_total_size > max_shard_size:
						# Make sure there is at least one row per partition.
						_lowerCamelCase				   =  min(snake_case__     ,  int(approx_total_size / max_shard_size		)		)
						_lowerCamelCase				   =  self.df.repartition(snake_case__		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     str     ,  snake_case__     :     str     ,  snake_case__     :     int     ,  )      ->	Iterable[Tuple[int, bool, Union[int, tuple]]]:
				import pyspark

				_lowerCamelCase				   =  ParquetWriter if file_format == 'parquet' else ArrowWriter
				_lowerCamelCase				   =  os.path.join(self._working_dir     ,  os.path.basename(snake_case__		)		) if self._working_dir else fpath
				_lowerCamelCase				   =  file_format == 'parquet'

				# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
				# pickling the SparkContext.
				_lowerCamelCase				   =  self.config.features
				_lowerCamelCase				   =  self._writer_batch_size
				_lowerCamelCase				   =  self._fs.storage_options

				def write_arrow(snake_case__     :     Any		):
						# Within the same SparkContext, no two task attempts will share the same attempt ID.
						_lowerCamelCase				   =  pyspark.TaskContext().taskAttemptId()
						_lowerCamelCase				   =  next(snake_case__     ,  snake_case__		)
						if first_batch is None:
								# Some partitions might not receive any data.
								return pa.RecordBatch.from_arrays(
								    [[task_id], [0], [0]]     ,  names=['task_id', 'num_examples', 'num_bytes']     ,  )
						_lowerCamelCase				   =  0
						_lowerCamelCase				   =  writer_class(
						    features=snake_case__     ,  path=working_fpath.replace('SSSSS'     ,  f"""{shard_id:05d}"""		).replace('TTTTT'     ,  f"""{task_id:05d}"""		)     ,  writer_batch_size=snake_case__     ,  storage_options=snake_case__     ,  embed_local_files=snake_case__     ,  )
						_lowerCamelCase				   =  pa.Table.from_batches([first_batch]		)
						writer.write_table(snake_case__		)
						for batch in it:
								if max_shard_size is not None and writer._num_bytes >= max_shard_size:
										_lowerCamelCase   ,							_lowerCamelCase				   =  writer.finalize()
										writer.close()
										yield pa.RecordBatch.from_arrays(
										    [[task_id], [num_examples], [num_bytes]]     ,  names=['task_id', 'num_examples', 'num_bytes']     ,  )
										shard_id += 1
										_lowerCamelCase				   =  writer_class(
										    features=writer._features     ,  path=working_fpath.replace('SSSSS'     ,  f"""{shard_id:05d}"""		).replace('TTTTT'     ,  f"""{task_id:05d}"""		)     ,  writer_batch_size=snake_case__     ,  storage_options=snake_case__     ,  embed_local_files=snake_case__     ,  )
								_lowerCamelCase				   =  pa.Table.from_batches([batch]		)
								writer.write_table(snake_case__		)

						if writer._num_bytes > 0:
								_lowerCamelCase   ,							_lowerCamelCase				   =  writer.finalize()
								writer.close()
								yield pa.RecordBatch.from_arrays(
								    [[task_id], [num_examples], [num_bytes]]     ,  names=['task_id', 'num_examples', 'num_bytes']     ,  )

						if working_fpath != fpath:
								for file in os.listdir(os.path.dirname(snake_case__		)		):
										_lowerCamelCase				   =  os.path.join(os.path.dirname(snake_case__		)     ,  os.path.basename(snake_case__		)		)
										shutil.move(snake_case__     ,  snake_case__		)

				_lowerCamelCase				   =  (
				    self.df.mapInArrow(snake_case__     ,  'task_id: long, num_examples: long, num_bytes: long'		)
				    .groupBy('task_id'		)
				    .agg(
				        pyspark.sql.functions.sum('num_examples'		).alias('total_num_examples'		)     ,  pyspark.sql.functions.sum('num_bytes'		).alias('total_num_bytes'		)     ,  pyspark.sql.functions.count('num_bytes'		).alias('num_shards'		)     ,  pyspark.sql.functions.collect_list('num_examples'		).alias('shard_lengths'		)     ,  )
				    .collect()
				)
				for row in stats:
						yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     "datasets.SplitGenerator"     ,  snake_case__     :     str = "arrow"     ,  snake_case__     :     Optional[Union[str, int]] = None     ,  snake_case__     :     Optional[int] = None     ,  **snake_case__     :     Union[str, Any]     ,  )      ->	Optional[int]:
				self._validate_cache_dir()

				_lowerCamelCase				   =  convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE		)
				self._repartition_df_if_needed(snake_case__		)
				_lowerCamelCase				   =  not is_remote_filesystem(self._fs		)
				_lowerCamelCase				   =  os.path.join if is_local else posixpath.join

				_lowerCamelCase				   =  '-TTTTT-SSSSS-of-NNNNN'
				_lowerCamelCase				   =  f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
				_lowerCamelCase				   =  path_join(self._output_dir     ,  snake_case__		)

				_lowerCamelCase				   =  0
				_lowerCamelCase				   =  0
				_lowerCamelCase				   =  0
				_lowerCamelCase				   =  []
				_lowerCamelCase				   =  []

				for task_id, content in self._prepare_split_single(snake_case__     ,  snake_case__     ,  snake_case__		):
						(
						    (
						    _lowerCamelCase
						)   ,							(
						    _lowerCamelCase
						)   ,							(
						    _lowerCamelCase
						)   ,							(
						    _lowerCamelCase
						)   ,							
						)				   =  content
						if num_bytes > 0:
								total_num_examples += num_examples
								total_num_bytes += num_bytes
								total_shards += num_shards
								task_id_and_num_shards.append((task_id, num_shards)		)
								all_shard_lengths.extend(snake_case__		)

				_lowerCamelCase				   =  total_num_examples
				_lowerCamelCase				   =  total_num_bytes

				# should rename everything at the end
				logger.debug(f"""Renaming {total_shards} shards."""		)
				if total_shards > 1:
						_lowerCamelCase				   =  all_shard_lengths

						# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
						# pickling error due to pickling the SparkContext.
						_lowerCamelCase				   =  self._fs

						# use the -SSSSS-of-NNNNN pattern
						def _rename_shard(
						    snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  ):
								rename(
								    snake_case__     ,  fpath.replace('SSSSS'     ,  f"""{shard_id:05d}"""		).replace('TTTTT'     ,  f"""{task_id:05d}"""		)     ,  fpath.replace('TTTTT-SSSSS'     ,  f"""{global_shard_id:05d}"""		).replace('NNNNN'     ,  f"""{total_shards:05d}"""		)     ,  )

						_lowerCamelCase				   =  []
						_lowerCamelCase				   =  0
						for i in range(len(snake_case__		)		):
								_lowerCamelCase   ,							_lowerCamelCase				   =  task_id_and_num_shards[i]
								for shard_id in range(snake_case__		):
										args.append([task_id, shard_id, global_shard_id]		)
										global_shard_id += 1
						self._spark.sparkContext.parallelize(snake_case__     ,  len(snake_case__		)		).map(lambda snake_case__		: _rename_shard(*snake_case__		)		).collect()
				else:
						# don't use any pattern
						_lowerCamelCase				   =  0
						_lowerCamelCase				   =  task_id_and_num_shards[0][0]
						self._rename(
						    fpath.replace('SSSSS'     ,  f"""{shard_id:05d}"""		).replace('TTTTT'     ,  f"""{task_id:05d}"""		)     ,  fpath.replace(snake_case__     ,  ''		)     ,  )



		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     "datasets.SplitGenerator"     ,  )      ->	SparkExamplesIterable:
				return SparkExamplesIterable(self.df		)
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A						=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default='cifar10'     ,metadata={'help': 'Name of a dataset from the datasets package'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'The column name of the images in the files.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the training data.'}			)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'A folder containing the validation data.'}			)
		lowerCAmelCase_     =   field(
		    default=0.1_5     ,metadata={'help': 'Percent to split off of train for validation.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }     ,)
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Dict:
				_lowerCamelCase				   =  {}
				if self.train_dir is not None:
						_lowerCamelCase				   =  self.train_dir
				if self.validation_dir is not None:
						_lowerCamelCase				   =  self.validation_dir
				_lowerCamelCase				   =  data_files if data_files else None





@dataclass
class     lowerCAmelCase__     :


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Override some existing default config settings when a model is trained from scratch. Example: '
		            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}			)
		lowerCAmelCase_     =   field(
		    default='main'     ,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,)
		lowerCAmelCase_     =   field(default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Name or path of preprocessor config.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }     ,)
		lowerCAmelCase_     =   field(
		    default=0.7_5     ,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}			)
		lowerCAmelCase_     =   field(
		    default=SCREAMING_SNAKE_CASE_     ,metadata={'help': 'Whether or not to train with normalized pixel values as target.'}			)





@dataclass
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   field(
		    default=1e-3     ,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)



def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    int:
		_lowerCamelCase				   =  torch.stack([example['pixel_values'] for example in examples]							)
		return {"pixel_values": pixel_values}


def 	lowerCamelCase					(	)   ->    Dict:
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		_lowerCamelCase				   =  HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)							)
		if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
		else:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry('run_mae'			,       UpperCamelCase			,       UpperCamelCase							)

		# Setup logging
		logging.basicConfig(
		    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,       datefmt='%m/%d/%Y %H:%M:%S'			,       handlers=[logging.StreamHandler(sys.stdout							)]			,       )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		_lowerCamelCase				   =  training_args.get_process_log_level()
		logger.setLevel(UpperCamelCase							)
		transformers.utils.logging.set_verbosity(UpperCamelCase							)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + F"""distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}"""							)
		logger.info(F"""Training/evaluation parameters {training_args}"""							)

		# Detecting last checkpoint.
		_lowerCamelCase				   =  None
		if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
				_lowerCamelCase				   =  get_last_checkpoint(training_args.output_dir							)
				if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    'Use --overwrite_output_dir to overcome.'							)
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'							)

    # Initialize our dataset.
		_lowerCamelCase				   =  load_dataset(
		    data_args.dataset_name			,       data_args.dataset_config_name			,       data_files=data_args.data_files			,       cache_dir=model_args.cache_dir			,       use_auth_token=True if model_args.use_auth_token else None			,       )

		# If we don't have a validation split, split off a percentage of train as validation.
		_lowerCamelCase				   =  None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split			,       UpperCamelCase							) and data_args.train_val_split > 0.0:
				_lowerCamelCase				   =  ds['train'].train_test_split(data_args.train_val_split							)
				_lowerCamelCase				   =  split['train']
				_lowerCamelCase				   =  split['test']

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_lowerCamelCase				   =  {
		    'cache_dir': model_args.cache_dir,
		    'revision': model_args.model_revision,
		    'use_auth_token': True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.config_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTMAEConfig()
				logger.warning('You are instantiating a new config instance from scratch.'							)
				if model_args.config_overrides is not None:
						logger.info(F"""Overriding config: {model_args.config_overrides}"""							)
						config.update_from_string(model_args.config_overrides							)
						logger.info(F"""New config: {config}"""							)

    # adapt config
		config.update(
		    {
		        'mask_ratio': model_args.mask_ratio,
		        'norm_pix_loss': model_args.norm_pix_loss,
		    }							)

		# create image processor
		if model_args.image_processor_name:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.image_processor_name			,       **UpperCamelCase							)
		elif model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,       **UpperCamelCase							)
		else:
				_lowerCamelCase				   =  ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				_lowerCamelCase				   =  ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool('.ckpt' in model_args.model_name_or_path							)			,       config=UpperCamelCase			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )
		else:
				logger.info('Training new model from scratch'							)
				_lowerCamelCase				   =  ViTMAEForPreTraining(UpperCamelCase							)

		if training_args.do_train:
				_lowerCamelCase				   =  ds['train'].column_names
		else:
				_lowerCamelCase				   =  ds['validation'].column_names

		if data_args.image_column_name is not None:
				_lowerCamelCase				   =  data_args.image_column_name
		elif "image" in column_names:
				_lowerCamelCase				   =  'image'
		elif "img" in column_names:
				_lowerCamelCase				   =  'img'
		else:
				_lowerCamelCase				   =  column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				_lowerCamelCase				   =  image_processor.size['shortest_edge']
		else:
				_lowerCamelCase				   =  (image_processor.size['height'], image_processor.size['width'])
		_lowerCamelCase				   =  Compose(
		    [
		        Lambda(lambda UpperCamelCase							: img.convert('RGB'							) if img.mode != "RGB" else img							),
		        RandomResizedCrop(UpperCamelCase			,       scale=(0.2, 1.0)			,       interpolation=InterpolationMode.BICUBIC							),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean			,       std=image_processor.image_std							),
		    ]							)

		def preprocess_images(UpperCamelCase     :				Tuple							):
				_lowerCamelCase				   =  [transforms(UpperCamelCase							) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError('--do_train requires a train dataset'							)
				if data_args.max_train_samples is not None:
						_lowerCamelCase				   =  ds['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
				# Set the training transforms
				ds["train"].set_transform(UpperCamelCase							)

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError('--do_eval requires a validation dataset'							)
				if data_args.max_eval_samples is not None:
						_lowerCamelCase				   =  (
						    ds['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
						)
				# Set the validation transforms
				ds["validation"].set_transform(UpperCamelCase							)

		# Compute absolute learning rate
		_lowerCamelCase				   =  (
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				_lowerCamelCase				   =  training_args.base_learning_rate * total_train_batch_size / 2_56

		# Initialize our trainer
		_lowerCamelCase				   =  Trainer(
		    model=UpperCamelCase			,       args=UpperCamelCase			,       train_dataset=ds['train'] if training_args.do_train else None			,       eval_dataset=ds['validation'] if training_args.do_eval else None			,       tokenizer=UpperCamelCase			,       data_collator=UpperCamelCase			,       )

		# Training
		if training_args.do_train:
				_lowerCamelCase				   =  None
				if training_args.resume_from_checkpoint is not None:
						_lowerCamelCase				   =  training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_lowerCamelCase				   =  last_checkpoint
				_lowerCamelCase				   =  trainer.train(resume_from_checkpoint=UpperCamelCase							)
				trainer.save_model()
				trainer.log_metrics('train'			,       train_result.metrics							)
				trainer.save_metrics('train'			,       train_result.metrics							)
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				_lowerCamelCase				   =  trainer.evaluate()
				trainer.log_metrics('eval'			,       UpperCamelCase							)
				trainer.save_metrics('eval'			,       UpperCamelCase							)

		# Write model card and (optionally) push to hub
		_lowerCamelCase				   =  {
		    'tasks': 'masked-auto-encoding',
		    'dataset': data_args.dataset_name,
		    'tags': ['masked-auto-encoding'],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**UpperCamelCase							)
		else:
				trainer.create_model_card(**UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any							)   ->    Dict:
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
				main()